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Record W4412521698 · doi:10.1136/jnis-2025-023713

Glymphatics in neurovascular diseases

2025· review· en· W4412521698 on OpenAlexaff
Tze Phei Kee, Timo Krings

Bibliographic record

VenueJournal of NeuroInterventional Surgery · 2025
Typereview
Languageen
FieldNeuroscience
TopicCerebrospinal fluid and hydrocephalus
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGlymphatic systemMedicineNeurovascular bundleCerebrospinal fluidNeuroscienceLymphatic systemPathologyBiology

Abstract

fetched live from OpenAlex

The glymphatic system is a brain-wide waste clearance mechanism that mimics lymphatic functions and facilitates the removal of amyloid aggregates. It is supposed to be essential for maintaining homeostasis within the central nervous system including nutrient delivery, waste removal, and consistency of the ionic microenvironment. While its dysfunction has been implicated in a variety of neurodegenerative disorders, its neurovascular implications are only slowly emerging. Driven by arterial pulsatility synchronized with the cardiac cycle, the system promotes cerebrospinal fluid (CSF) influx through perivascular spaces, modulated by aquaporin-4 channels in astrocytes. Waste-laden fluid then drains via perivenous spaces to meningeal lymphatics and cervical lymph nodes. Dysfunctions in this system have been implicated in neurovascular conditions, including subarachnoid hemorrhage, idiopathic intracranial hypertension, steno-occlusive disease, and arteriovenous shunting disorders. These diseases disrupt glymphatic flow through altered pulsatility, impaired CSF influx, aquaporin-4 malfunction, or venous hypertension. Such impairments lead to waste accumulation, contributing to progressive cognitive decline, which may be reversible with targeted interventions. This review underscores the role of the glymphatic system in neurovascular diseases and highlights potential endovascular avenues to mitigate cognitive impairment due to an impaired glymphatic system.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.932
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.005
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.069
GPT teacher head0.340
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2025
Admission routes1
Has abstractyes

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